Skip to main content

Advertisement

SpringerLink
Log in

Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm

  • ORIGINAL PAPER
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Clinical pathways’ variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating diseases or even endanger patients’ life if not handled effectively. In order to improve the accuracy and efficiency of variances handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimization algorithm (RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Bragato, L., and Jacobs, K., Care pathways: the road to better health services. J. Health Organ. Manage. 17(3):164–180, 2003.

    Article  Google Scholar 

  2. Cheah, J., Development and implementation of a clinical pathway programme in an acute care general hospital in Singapore. Int. J. Qual. Health Care 12:403–412, 2000.

    Article  Google Scholar 

  3. Hunter, B., and Segrott, J., Re-mapping client journeys and professional identities: a review of the literature on clinical pathways. Int. J. Nurs. Stud. 45(4):608–625, 2008.

    Article  Google Scholar 

  4. Wakamiya, S. J., and Yamauchi, K., What are the standard functions of electronic clinical pathways? Int. J. Med. Inform. 78(8):543–550, 2009.

    Article  Google Scholar 

  5. Du, G., Jiang, Z. B., Diao, X. D., Ye, Y., Yao, Y., Modeling, variation monitoring, analyzing, reasoning for intelligently reconfigurable clinical pathway. Proceedings of the IEEE International Conference on Service Operations, Logistics and Informatics, 85–90, Chicago, IL, USA, 2009.

  6. Wigfield, A., and Boon, E., Critical care pathway development: the way forward. Br. J. Nurs. 5(12):732–735, 1996.

    Google Scholar 

  7. Cheah, J., Clinical pathways: changing the face of client care delivery in the next millennium. Clinician Manag. 7(78):78–84, 1998.

    Google Scholar 

  8. Price, M. B., Jones, A., Hawkins, J. A., et al., Critical pathways for postoperative care after simple congenital heart surgery. AJMC 5:185–192, 1999.

    Google Scholar 

  9. Atwal, A., and Caldwell, K., Do multidisciplinary integrated care pathways improve interprofessional collaboration? Scand. J. Caring Sci. 16(4):360–367, 2002.

    Article  Google Scholar 

  10. Bryan, S., Holmes, S., Prostlethwaite, D., and Carty, N., The role of integrated care pathways in improving the client experience. Prof. Nurse 18(2):77–79, 2002.

    Google Scholar 

  11. Caminiti, C., Scoditti, U., Diodati, F., and Passalacqua, R., How to promote, improve and test adherence to scientific evidence in clinical practice. BMC Health Serv. Res. 5(62):1–11, 2005.

    Google Scholar 

  12. Wakamiya, S., and Yamauchi, K., A new approach to systematization of the management of paper-based clinical pathways. Comput. Meth. Programs Biomed. 82:169–176, 2006.

    Article  Google Scholar 

  13. Ye, Y., Jiang, Z. B., Diao, X. D., Yang, D., and Du, G., An ontology-based hierarchical semantic modeling approach to clinical pathway workflows. Comput. Biol. Med. 39(8):722–732, 2009.

    Article  Google Scholar 

  14. Ye, Y., Jiang, Z. B., Diao, X. D., Du, G., Knowledge-based hybrid variance handling for patient care workflows based on clinical pathways. Proceedings of the IEEE International Conference on Service Operations, Logistics and Informatics, 13–18, Chicago, IL, USA, 2009.

  15. Ye, Y., Jiang, Z. B., Diao, X. D., Du, G., A Semantics-Based Clinical Pathway Workflow and Variance Management Framework, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, 758–762, Bei Jing, China, 2008.

  16. Du, G., Jiang, Z. B., Diao, X. D., Sun, Y. J., Ye, Y., and Yao, Y., Adaptive workflow engine based on rule for clinical pathway. Journal of Shanghai Jiao Tong University 43(7):1021–1026, 2009.

    Google Scholar 

  17. Er, O., Yumusak, N., and Temurtas, F., Chest diseases diagnosis using artificial neural networks. Expert Syst. Appl. 37(12):7648–7655, 2010.

    Article  Google Scholar 

  18. Chen, Y., Yang, B., Abraham, A., and Peng, L., Automatic design of hierarchical Takagi–Sugeno fuzzy systems using evolutionary algorithms. IEEE Trans. Fuzzy Syst. 15(3):385–397, 2007.

    Article  MATH  Google Scholar 

  19. Yoon, Y., Guimaraes, T., and Swales, G., Integrating artificial neural networks with rule-based expert systems. Decis. Support Syst. 11(5):497–507, 1994.

    Article  Google Scholar 

  20. Takagi, T., and Sugeno, M., Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15:116–132, 1985.

    MATH  Google Scholar 

  21. Lin, F. J., Lin, C. H., and Shen, P. H., Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. IEEE Trans. Fuzzy Sys. 9(5):751–759, 2001.

    Article  Google Scholar 

  22. Jang, J. S. R., ANFIS: Adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3):665–684, 1993.

    Article  MathSciNet  Google Scholar 

  23. Pedrycz, W., and Reformat, M., Evolutionary fuzzy modeling. IEEE Trans. Fuzzy Syst. 11(5):652–665, 2003.

    Article  Google Scholar 

  24. Oh, S. K., Pedrycz, W., and Park, H. S., Hybrid id entification in fuzzy neural networks. Fuzzy Sets Syst. 138:399–426, 2003.

    Article  MathSciNet  Google Scholar 

  25. Wang, L., and Yen, J., Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter. Fuzzy Sets Syst. 101:353–362, 1999.

    Article  MathSciNet  Google Scholar 

  26. Wang, H., Kwong, S., Jin, Y., Wei, W., and Man, K. F., Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets Syst. 149(1):149–186, 2005.

    Article  MathSciNet  MATH  Google Scholar 

  27. Tang, A. M., Quek, C., and Ng, G. S., GA-TSKfnn: parameters tuning of fuzzy neural network using genetic algorithms. Expert Syst. Appl. 29:769–781, 2005.

    Article  Google Scholar 

  28. Lin, C. J., and Xu, Y. J., A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications. Fuzzy Sets Syst. 157:1036–1056, 2006.

    Article  MathSciNet  MATH  Google Scholar 

  29. Jelodar, M. S., Kamal, M., Fakhraie, S. M., Ahmadabadi, M. N., SOPC-based parallel genetic algorithm. Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 2006.

  30. Kennedy, J., Eberhart, R C., Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks. Piscataway, 1942–1948, 1995.

  31. Fourie, P. C., and Groenwold, A. A., The particle swarm optimization algorithm in size and shape. Struct. Multidiscip. O. 23(4):259–267, 2002.

    Article  Google Scholar 

  32. Han, M., Sun, Y. N., and Fan, Y. N., An improved fuzzy neural network based on T-S model. Expert Syst. Appl. 34:2905–2920, 2008.

    Article  Google Scholar 

  33. Khosla, A., Kumar, S., Aggarwal, K. K., A framework for identification of fuzzy models through particle swarm optimization, Proceedings of the IEEE Indicon Conference, 388–391, Chennai, India, 2005.

  34. Khosla, A., Kumar, S., Ghosh, K. R., A comparison of computational efforts between particle swarm optimization and genetic algorithm for identification of fuzzy models, in: Annual Conference of the North American Fuzzy Information Processing Society-NAFIPS, 245–250, 2007.

  35. Shoorehdeli, M. A., Teshnehlab, M., and Sedigh, A. K., Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter. Expert Syst. Appl. 160:922–948, 2009.

    MathSciNet  MATH  Google Scholar 

  36. Shi, Y., and Eberhart, R. C., Empirical study of particle swarm optimization. Proc. IEEE Int. Conf. Evolutionary Computation 3:101–106, 1999.

    Google Scholar 

  37. Ratnaweera, A., Halgamuge, S. K., and Watoson, H. C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient. IEEE Trans. Evol. Comput. 8(3):240–255, 2004.

    Article  Google Scholar 

  38. Lovbjerg, M., Rasmussen, T. K., Krink, T., Hybrid particle swarm optimizer with breeding and sub-populations. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). San Francisco, CA, July 2001.

  39. Xie, X. F., Zhang, W. J., et al., Optimizing semiconductor devices by self-organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA, 2017–2022, 2004.

  40. He, R., Wang, Y., et al., An improved particle swarm optimization based on self-adaptive escape velocity. JSW 16(12):2036–2044, 2005.

    Article  MATH  Google Scholar 

  41. Lin, C.-J., Wang, J.-G., and Lee, C.-Y., Pattern recognition using neural-fuzzy networks based on improved particle swam optimization. Expert Syst. Appl. 36:5402–5410, 2009.

    Article  Google Scholar 

  42. Lin, C.-J., An efficient immune-based symbiotic particle swarm optimization learning algorithm for TSK-type neuro-fuzzy networks design. Fuzzy Sets Syst. 159:2890–2909, 2008.

    Article  Google Scholar 

  43. Van den Bergh, F., and Engelbrecht, A. P., A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3):225–239, 2004.

    Article  Google Scholar 

  44. Niu, B., Zhu, Y. L., He, X. X., and Shena, H., A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing. Neurocomputing 71:1436–1448, 2008.

    Article  Google Scholar 

  45. Higashi, N., Iba, H., Particle swarm optimization with Gaussian mutation. Proceedings of the IEEE Swarm Intelligence Symp. Indianapolis: IEEE Inc, 72–79, 2003.

  46. Kennedy, J., Eberhart, R. C., A discrete binary version of the particle swarm algorithm, systems, man, and cybernetics, Computational Cybernetics and Simulation IEEE International Conference, 5 (12–15): 4104–4108, 1997.

  47. Simon, D., Training fuzzy systems with the extended Kalman filter. Fuzzy Sets Syst. 132:189–199, 2002.

    Article  MATH  Google Scholar 

  48. Coffin, M., and Saltzman, Statistical analysis of computational tests of algorithms and heuristics. INFORMS J. Comput. 12(1):24–44, 2000.

    Article  MATH  Google Scholar 

Download references

Acknowledgement

This work described in this paper was supported by Research Grant from National Natural Science Foundation of China (60774103) and Major Program Development Fund of SJTU. Moreover, we would also like to thank to the whole medical staff of Shanghai No. 6 People’s Hospital for real data collecting and helpful discussions.

The authors would like to express sincere appreciation to the journal editor and two anonymous referees for their detailed and helpful comments to improve the quality of the paper.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering & Logistics Management, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 200240, China

    Gang Du, Zhibin Jiang & Yan Ye

  2. Shanghai Putuo District Central Hospital, Shanghai, 200062, China

    Xiaodi Diao

  3. Shanghai No. 6 People’s Hospital, Shanghai, 200233, China

    Yang Yao

Authors
  1. Gang Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhibin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaodi Diao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhibin Jiang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Du, G., Jiang, Z., Diao, X. et al. Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm. J Med Syst 36, 1283–1300 (2012). https://doi.org/10.1007/s10916-010-9589-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9589-6

Keywords

Search

Navigation